MOTIVATION: Blood-based profiling of tumor DNA ("liquid biopsy") offers great prospects for non-invasive early cancer diagnosis and clinical guidance, but requires further computational advances to become a robust quantitative assay of tumor clonal evolution. We propose new methods to better characterize tumor clonal dynamics from circulating tumor DNA (ctDNA), through application to two specific tasks: (i) applying longitudinal ctDNA data to refine phylogeny models of clonal evolution, and (ii) quantifying changes in clonal frequencies that may be indicative of treatment response or tumor progression. We pose these through a probabilistic framework for optimally identifying markers and using them to characterize clonal evolution. RESULTS: We first estimate a density over clonal tree models using bootstrap samples over pre-treatment tissue-based sequence data. We then refine these models over successive longitudinal samples. We use the resulting framework for modeling and refining tree densities to pose a set of optimization problems for selecting ctDNA markers to maximize measures of utility for reducing uncertainty in phylogeny models and quantifying clonal frequencies given the models. We tested our methods on synthetic data and showed them to be effective at refining tree densities and inferring clonal frequencies. Application to real tumor data further demonstrated the methods' effectiveness in refining a lineage model and assessing its clonal frequencies. The work shows the power of computational methods to improve marker selection, clonal lineage reconstruction, and clonal dynamics profiling for more precise and quantitative assays of somatic evolution and tumor progression. AVAILABILITY AND IMPLEMENTATION: https://github.com/CMUSchwartzLab/Mase-phi.git. (DOI: 10.5281/zenodo.14776163).

In-air gesture control extends a touch screen and enables contactless interaction, thus has become a popular research direction in the past few years. Prior work has implemented this functionality based on cameras, acoustic signals, and Wi-Fi via existing hardware on commercial devices. However, these methods have low user acceptance. Solutions based on cameras and acoustic signals raise privacy concerns, while WiFi-based solutions are vulnerable to background noise. As a result, these methods are not commercialized and recent flagship smartphones have implemented in-air gesture recognition by adding extra hardware on-board, such as mmWave radar and depth camera. The question is, can we support in-air gesture control on legacy devices without any hardware modifications? To answer this question, in this work, we propose, an in-air gesture recognition system leveraging the screen and ambient light sensor (ALS), which are ordinary modalities on mobile devices. For the transmitter side, we design a screen display mechanism to embed spatial information and preserve the viewing experience; for the receiver side, we develop a framework to recognize gestures from low-quality ALS readings. We implement and evaluate on both a tablet and several smartphones. Results show that can recognize$9$types of frequently used in-air gestures with an average accuracy of$96.1\%$.

Spectrum sensing plays a crucial role in spectrum monitoring and management. However, due to the expensive cost of high-speed ADCs, wideband spectrum sensing is a long-standing challenge. In this paper, we present how to transform Ultra-wideband (UWB) devices into a spectrum sensor which can provide wideband spectrum monitoring at a low cost. Compared with the expensive high-speed ADCs which cost at least hundreds of dollars, a UWB device is only several dollars. As the low-cost UWB technology is not originally designed for spectrum sensing, we address the inherent limitations of low-cost devices such as limited memory, low SPI speed and low accuracy, and show how to obtain spectrum occupancy information from the noisy and spurious UWB channel impulse response. In this paper, we present, which not only can give accurate channel occupancy information, but also can precisely estimate the signal power and bandwidth. can also detect fleeting radar signals. We implement and perform extensive evaluations with both controlled experiments and field tests. Results show that can sense up to$900$MHz bandwidth with frequency range from 0.5GHz to 7GHz, and the power estimation error is less than$3$dB. can also accurately detect busy 5G channels and can classify the encrypted traffic from the channel usage patterns. We believe that provides a new paradigm for low-cost wideband spectrum sensing, which is critical for large-scale fine-grained spectrum monitoring.

Wireless home surveillance cameras are gaining popularity in elderly/baby care and burglary detection in an effort to make our life safer than ever before. However, even though the camera’s traffic is encrypted, attackers can still infer what the residents are doing at home. Although this security loophole has been reported, it does not attract much attention from the public, as it requires the attacker to be in close proximity to the camera and have some prior knowledge about the victims. Due to these requirements, the attacker has a low chance of success in the real world. In this article, we argue that the capability of attackers has been greatly underestimated. First, the attacker can leverage the characteristics of video transport protocols to recover the metadata of missing packets. Second, the attacker can build the inference model using the public datasets and adapt the model to the real traffic. Thus, the attacker can launch the attack at a distance from the camera, without prior knowledge about the victim. We also implement this attack scenario and verify that the attacker can infer the victims’ activities at a distance as large as 40 m without any knowledge about the victim, neither personal nor environmental.

Spectrum sensing plays a crucial role in spectrum monitoring and management. However, due to the expensive cost of high-speed ADCs, wideband spectrum sensing is a long-standing challenge. In this paper, we present how to transform Ultra-wideband (UWB) devices into a spectrum sensor which can provide wideband spectrum monitoring at a low cost. Compared with the expensive high-speed ADCs which cost at least hundreds of dollars, a UWB device is only several dollars. As the low-cost UWB technology is not originally designed for spectrum sensing, we address the inherent limitations of low-cost devices such as limited memory, low SPI speed and low accuracy, and show how to obtain spectrum occupancy information from the noisy and spurious UWB channel impulse response. In this paper, we present WISE, which not only can give accurate channel occupancy information, but also can precisely estimate the signal power and bandwidth. WISE can also detect fleeting radar signals. We implement WISE and perform extensive evaluations with both controlled experiments and field tests. Results show that WISE can sense up to 900MHz bandwidth and the power estimation error is less than 3dB. WISE can also accurately detect busy 5G channels. We believe that WISE provides a new paradigm for low-cost wideband spectrum sensing, which is critical for large-scale fine-grained spectrum monitoring.

Spectrum sensing plays a crucial role in spectrum monitoring and management. However, Due to the expensive cost of high-speed ADCs, wideband spectrum sensing is a long-standing challenge. In this demo, we present how to transform the low-cost Ultrawideband (UWB) devices into a spectrum sensor and showcase WISE, a low-cost wideband spectrum sensing system, which not only can give accurate channel occupancy information, but also can precisely estimate the signal power and bandwidth. Our demo will show that WISE can sense up to 900MHz bandwidth and the power estimation error is less than 3dB. WISE can also accurately detect busy 5G channels and fleeting radar signals. We believe that WISE provides a new paradigm for low-cost wideband spectrum sensing, which is critical for large-scale fine-grained spectrum monitoring.

In-air gesture control extends a touch screen and enables contactless interaction, thus has become a popular research direction in the past few years. Prior work has implemented this functionality based on cameras, acoustic signals, and Wi-Fi via existing hardware on commercial devices. However, these methods have low user acceptance. Solutions based on cameras and acoustic signals raise privacy concerns, while WiFi-based solutions are vulnerable to background noise. As a result, these methods are not commercialized and recent flagship smartphones have implemented in-air gesture recognition by adding extra hardware on-board, such as mmWave radar and depth camera. The question is, can we support in-air gesture control on legacy devices without any hardware modifications?

In-air gesture control extends a touch screen and enables contact-less interaction, thus has become a popular research direction in the past few years. Prior work has implemented this functionality based on cameras, acoustic signals, and Wi-Fi via existing hardware on commercial devices. However, these methods have low user acceptance. Solutions based on cameras and acoustic signals raise privacy concerns, while WiFi-based solutions are vulnerable to background noise. As a result, these methods are not commercialized and recent flagship smartphones have implemented in-air gesture recognition by adding extra hardware on-board, such as mmWave radar and depth camera. The question is, can we support in-air gesture control on legacy devices without any hardware modifications?

Known as input in the numerical weather prediction (NWP) models, microwave radiation imager (MWRI) data have been widely distributed to the user community. Nevertheless, the current operational geolocation accuracy is still on the pixel scale due to the presence of geolocation uncertainty. In this letter, we propose a new method to estimate the geolocation errors in MWRI data. Compared to the traditional coastline inflection method (CIM), the proposed method has two innovations. First, we establish a surface fitting interpolation model by involving more observations to detect the coastline. Second, we employ the iterative closest point (ICP) algorithm to determine the correspondences between the detected coastline and the actual coastline. Simulated experimental results demonstrate that the proposed method can provide a more accurate geolocation error estimation than the CIM. By applying our method, we have processed an MWRI data set from January 1 to February 28 in 2016. The experimental results have shown that the operational FY-3C MWRI geolocation errors are 0.4813 and 0.4909 pixels in the along-track and cross-track directions, respectively, which can be significantly reduced to 0.1299 and 0.1497 pixels after the attitude correction. It means that the geolocation accuracy has an average improvement up to 70%.

The microwave radiation imager (MWRI) located onboard the FengYun-3C (FY-3C) satellite provides a considerable amount of critical information for numerical weather predictions. Obtaining accurate geolocation results from the FY-3C MWRI data is of great importance. In this letter, we improve the traditional coastline inflection method (CIM) and propose an$\ell _{0}$sparse approximation model for geolocation error estimation and correction. Specifically, we propose using the jump point of the step function to estimate the true coastline point. This approach can characterize the geolocation errors more accurately than the CIM, which further improves the geolocation accuracy. In the theoretical part, we provide a complete solution to obtain the step function through an iterative blind deconvolution. For a practical use, we demonstrate the effectiveness of the proposed method for geolocation error estimation through quantitative results obtained on the FY-3C MWRI data. The experimental results show that the proposed method can achieve an improvement of up to 33.33% in the standard deviation of geolocation errors (approximately 0.00030) compared to the traditional CIM (approximately 0.00045). Furthermore, we also apply the proposed method to the FY-3C satellite and improve the geolocation accuracy of the MWRI data through geolocation error correction.

We study and solve the problem of classical channel simulation with quantum side information at the receiver. This is a generalization of both the classical reverse Shannon theorem, and the classical-quantum Slepian-Wolf problem. The optimal noiseless communication rate is found to be reduced from the mutual information between the channel input and output by the Holevo information between the channel output and the quantum side information. Our main theorem has two important corollaries. The first is a quantum generalization of the Wyner-Ziv problem: rate-distortion theory with quantum side information. The second is an alternative proof of the tradeoff between classical communication and common randomness distilled from a quantum state. The fully quantum generalization of the problem considered isquantumstateredistribution. Here the sender and receiver share a mixed quantum state and the sender wants to transfer part of her state to the receiver using entanglement and quantum communication. We present outer and inner bounds on the achievable rate pairs.